A vector heterogeneous autoregressive index model for realized volatility measures
Gianluca Cubadda (),
Barbara Guardabascio () and
Alain Hecq ()
International Journal of Forecasting, 2017, vol. 33, issue 2, 337-344
This paper introduces a new model for detecting the presence of commonalities in a set of realized volatility measures. In particular, we propose a multivariate generalization of the heterogeneous autoregressive model (HAR) that is endowed with a common index structure. The vector heterogeneous autoregressive index model has the property of generating a common index that preserves the same temporal cascade structure as in the HAR model, a feature that is not shared by other aggregation methods (e.g., principal components). The parameters of this model can be estimated easily by a proper switching algorithm that increases the Gaussian likelihood at each step. We illustrate our approach using an empirical analysis that aims to combine several realized volatility measures of the same equity index for three different markets.
Keywords: Common volatility; HAR models; Index models; Combinations of realized volatilities; Forecasting (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: A Vector Heterogeneous Autoregressive Index Model for Realized Volatily Measures (2016)
Working Paper: A Vector Heterogeneous Autoregressive Index model for realized volatility measures (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:2:p:337-344
Access Statistics for this article
International Journal of Forecasting is currently edited by R. J. Hyndman
More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().